7588 papers
DocCogito is a unified framework for document understanding that aligns global layout perception with structured, region-grounded reasoning through a lightweight layout tower and a deterministic Visual-Semantic Chain, achieving state-of-the-art performance on multiple benchmarks by enforcing systematic coupling between layout priors and evidence-based reasoning.
This paper proposes the Hierarchical Autonomy Evolution (HAE) framework to address critical security vulnerabilities in evolving AI agents by categorizing threats and defenses across three tiers: Cognitive, Execution, and Collective Autonomy.
This paper proposes AMR-CCR, an anchored modular retrieval framework with script-conditioned injection and multi-prototype dictionaries to address the challenges of continual, class-incremental ancient Chinese character recognition, accompanied by the new EvoCON benchmark for systematic evaluation.
This paper proposes an innovative inverse-dynamics observer that integrates a linear single-track vehicle model with a distributed tire representation described by hyperbolic partial differential equations to accurately estimate sideslip angles and tire forces using only yaw rate and lateral acceleration measurements, even under noise and model uncertainties.
SeDa is a unified framework that aggregates over 7.6 million datasets from more than 200 platforms to enable trustworthy, semantically enriched, and multi-entity augmented exploration through standardized metadata, a dynamic tag graph, and provenance assurance.
This paper proposes a skeletal latent diffusion framework that leverages a differentiable skeletonization module and a large-scale MedSDF dataset to achieve high-fidelity, computationally efficient medical shape generation while effectively addressing challenges posed by anatomical geometric complexity and data scarcity.
This paper presents an end-to-end framework analyzing the r/Brexit subreddit to demonstrate that political polarization on Reddit is driven by self-selection and echo chambers, where user opinions become entrenched rather than softened by cross-cutting exposure.
The paper proposes EvolveReason, a self-evolving reasoning paradigm that combines a human-like chain-of-thought framework, a forgery latent-space distribution capture module, and a reinforcement learning-based self-evolution strategy to enhance the accuracy, detail, and reliability of explainable deepfake facial image identification.
InterReal is a unified physics-based imitation learning framework that enables humanoid robots to robustly learn and execute complex human-object interaction skills in real-world settings through a novel motion data augmentation scheme and an automatic reward learner.
The paper proposes GP-Tree, a novel in-memory spatial index that combines adaptive grid cells with a prefix tree structure to replace coarse minimum bounding rectangles with fine-grained approximations, thereby significantly improving filtering accuracy and query performance for complex spatial objects compared to traditional indexes.
This paper presents the first extensive study evaluating over 500 models to demonstrate that graph-based code representations consistently outperform other methods in predicting patch correctness, thereby significantly improving the effectiveness of automated program repair tools.
This paper introduces SketchGraphNet, a memory-efficient hybrid graph transformer that models free-hand sketches as structured graphs to achieve state-of-the-art recognition accuracy on the newly constructed 3.44-million-sample SketchGraph benchmark while significantly reducing computational resource requirements.
The paper presents ICLR, a novel framework that enhances in-context imitation learning for robots by augmenting demonstration prompts with structured visual reasoning traces and jointly training a unified autoregressive transformer to predict both future trajectories and actions, thereby improving success rates and generalization in complex manipulation tasks.
MIRO is a privacy-preserving framework that combines distributed particulate matter sensors with a multi-radar mmWave re-identification system, utilizing GAN-based view adaptation to track workers and estimate their specific exposure to airborne pollutants in industrial environments without relying on visual data.
The paper proposes ACCURATE, a robust 3D reconstruction framework that combines image segmentation with geometry-constrained topology traversal and dynamic programming to achieve high-accuracy reconstruction of arbitrary-shaped, deformable continuum bodies like guidewires and catheters under biplanar X-ray imaging.
This paper proposes a semantic geometric framework that leverages small vehicles as metric anchors within a decoupled stereoscopic projection model to recover absolute scale from monocular UAV images, thereby enabling scale-adaptive satellite image cropping and significantly improving cross-view geo-localization robustness under real-world scale ambiguity.
This paper introduces UniLongGen, a training-free inference strategy that improves long-horizon interleaved image generation by dynamically curating context to discard accumulated visual noise, thereby overcoming the reliability collapse caused by dense visual token interference in unified multimodal models.
The paper introduces CONSTANT, a novel one-shot handwriting generation framework that leverages Style-Aware Quantization and a latent patch-based contrastive objective within a diffusion model to overcome existing limitations in capturing diverse writer styles and generating high-quality, realistic handwritten images across multiple languages.
This paper evaluates the trade-off between privacy and Parkinson's disease detection in anonymized speech, demonstrating that while STT-TTS anonymization severely degrades diagnostic performance by erasing prosodic cues, kNN-VC effectively preserves macro-prosodic features to maintain high detection accuracy with only a minor performance drop.
This paper introduces a novel Speech Generation Speaker Poisoning (SGSP) framework to address privacy risks in zero-shot text-to-speech by modifying trained models to prevent the generation of specific speaker identities while maintaining utility for others, demonstrating effective protection for up to 15 speakers but revealing scalability challenges with larger sets due to identity overlap.